Nonlinear vs. nonstationary of hysteresis in unemployment: evidence from OECD economies
Bibliographic record
Abstract
This study examines the lower and higher boundaries for the threshold value to be considered an indicator of unemployment in a specific country. Specially, the objective is to conduct the critical moment of hysteresis effects happening in unemployment rate using a group of 16 OECD countries. The methodological strategy applies a developed tool of threshold tests involving unit root against stationary but nonlinear alternative by Caner and Hansen (2001 Caner, M. and Hansen, B. E. 2001. Threshold autoregressions with a unit root. Econometrica, 69: 1555–96. [Crossref], [Web of Science ®] , [Google Scholar]). A significant contribution of this study is identifying a trigger point from the nonstationary of time series process for the first time in the literature. Our empirical results finds strong evidence of the existence of nonlinear stationary in Australia, Canada, Finland, France, Germany, Ireland, Japan, Netherlands and the USA when the threshold effect holds. The hysteresis hypothesis is further confirmed by the fact that the unemployment rate exceeds the boundaries of the band, for Australia, Finland, France, Germany, Japan and the USA when the threshold unit root test of Caner and Hansen is rigorously implemented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".